Population structure of bigmouth buffalo (Ictiobus cyprinellus) across Canada and the United States
Bibliographic record
Abstract
Bigmouth Buffalo (Ictiobus cyprinellus) is an understudied large-bodied fish species that can live over 120 years, native to central North America. Bigmouth buffalo’s distribution ranges from Saskatchewan, Canada, to the Gulf of Mexico, and are particularly widely distributed within the Mississippi River basin in the US. Within Canada, they are divided into two populations: the Saskatchewan-Nelson River population in the Canadian prairies, and the Great Lakes-upper St. Lawrence River population in Ontario. The Saskatchewan-Nelson River population is listed as a species of special concern due to observed declines within the Qu’Appelle River, understanding if there is genetic mixing between Saskatchewan and Manitoba was one of the research priorities recommended in the 2019 species at risk management plan. Furthermore, bigmouth buffalo have become a popular sport fish in the US but lack harvest limits across most of the US. This study aimed to resolve the lack of population genetic structure of bigmouth buffalo across much of their range. I used restriction site-associated DNA sequencing to examine signatures of population divergence across five geographic areas, Minnesota and Missouri in the US, and Ontario, Manitoba, and Saskatchewan in Canada. Filtering of raw data followed the de novo stacks pipeline with a final data set of 12,071 single nucleotide polymorphisms (SNPs). I analysed the genetic data with observed and expected heterozygosity’s, inbreeding co-efficient, pairwise and population specific Fst, principal components analysis, admixture analysis, analysis of molecular variance, effective population size, SNPs under selection, and assignment accuracy to population of origin. I found evidence for population structure between the five locations with unidirectional admixture from Saskatchewan to Manitoba. Bigmouth buffalo had low genetic diversity suggesting an ancestral population bottleneck during the last glacial period or small recolonizing populations leading to founder effects in the populations following glacial retreat and re-colonization. Furthermore, I found low effective population size for this species, common in species like bigmouth buffalo that display episodic breeding, high fecundity, iteroparity, and low survivorship to age of maturation. These results have important implications for bigmouth buffalo management and provides an initial assessment of the population structure throughout much of their native range.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".